Cluster Analysis for Applications

By M. Anderberg. 1973

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Citations in the corpus

Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting (2020, methodology): “This is similar to initializing the k -means cluster centroids with points from the data set [1].”
Static pickup and delivery problems: a classification scheme and survey (2007, methodology): “In the first phase clusters of customers are formed using the average linkage method ( Anderberg 1973 ).”
GelJ – a tool for analyzing DNA fingerprint gel images (2015, methodology): “GelJ offers different methods to construct dendrograms based on hierarchical clustering [10]: UPGMA, UPGMC, single linkage, complete linkage, mean linkage, and Ward.”
Survey of clustering algorithms (2005, background): “We offer more detailed discussion on how to identify appropriate number of clusters, which is particularly important in cluster validity, in the last part of the section.”
A similarity coefficient measure and machine-parts grouping in cellular manufacturing systems (2000, background): “This coe cient is sensitive to the direction of coding, meaning that interchanging the elements 1 and 0 in an incident matrix usually changes the similarity values between the objects, and the same is true for the Ochiai coe cient (Anderberg 1973), the Sorenson coe cient and the Russell and Rao coe ”
Algorithms for hierarchical clustering: an overview, II (2017, background): “The latter may be described as a “stored dissimilarities approach” (Anderberg, 1973).”
Comparisons among clustering techniques for electricity customer classification (2006, background): “In hierarchical clustering [13], there are initially singleton clusters, as much as the number of RLPs.”
Distance measures for point sets and their computation (1997, background): “Questions of this type arise in several areas, such as cluster analysis [2, 20], computational geometry [1], philosophy of science [17, 18, 13, 15], updating and revising theories [6, 25], arbitration between theories [19], and machine learning [12, 11, 26].”
A proposed unified terminology of species traits in stream ecology (2015, background): “The only statement we can make on 2 states is whether they agree (=) or not (≠) (Anderberg 1973).”
Euclidean Distance as a Similarity Metric for Principal Component Analysis (2001, unknown): “Cluster analysis uses ED extensively to identify or group data or entities that are dissimilar (Anderberg 1973; Gong and Richman 1994).”
Geometric Filtration Using Proper Orthogonal Decomposition for Aerodynamic Design Optimization (2010, methodology): “The airfoils are selected using a KMEANS [39] clustering algorithm in an attempt to maintain a measure of diversity between the selected airfoils.”
Development of the practice environment scale of the Nursing Work Index. (2002, methodology): “In the ﬁnal stage the generalizability of the selected subscale structure was evaluated by an oblique multiple-group principal-components cluster analysis (Anderberg, 1973; Harman, 1976).”
Resilience Processes in Adolescents: Personality Profiles, Self-Worth, and Coping (2003, background): “More information about this analysis can be found in Hartigan (1975), MacQueen (1967), and Anderberg (1973). The number of clusters is specified by the researcher.”